Long only 1/n portfolio#
import pandas as pd
pd.options.plotting.backend = "plotly"
import yfinance as yf
from cvx.simulator.builder import builder
from cvx.simulator.grid import resample_index
# Get rid of findfont: Font family 'Arial' not found.
# when running a remote notebook on Jupyter Server on Ubuntu Linux server
import logging
logging.getLogger("matplotlib.font_manager").setLevel(logging.ERROR)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[1], line 2
1 import pandas as pd
----> 2 pd.options.plotting.backend = "plotly"
4 import yfinance as yf
6 from cvx.simulator.builder import builder
File ~/work/cvxjson/cvxjson/.venv/lib/python3.10/site-packages/pandas/_config/config.py:224, in DictWrapper.__setattr__(self, key, val)
221 # you can't set new keys
222 # can you can't overwrite subtrees
223 if key in self.d and not isinstance(self.d[key], dict):
--> 224 _set_option(prefix, val)
225 else:
226 raise OptionError("You can only set the value of existing options")
File ~/work/cvxjson/cvxjson/.venv/lib/python3.10/site-packages/pandas/_config/config.py:160, in _set_option(*args, **kwargs)
158 o = _get_registered_option(key)
159 if o and o.validator:
--> 160 o.validator(v)
162 # walk the nested dict
163 root, k = _get_root(key)
File ~/work/cvxjson/cvxjson/.venv/lib/python3.10/site-packages/pandas/core/config_init.py:643, in register_plotting_backend_cb(key)
640 return
641 from pandas.plotting._core import _get_plot_backend
--> 643 _get_plot_backend(key)
File ~/work/cvxjson/cvxjson/.venv/lib/python3.10/site-packages/pandas/plotting/_core.py:1862, in _get_plot_backend(backend)
1859 if backend_str in _backends:
1860 return _backends[backend_str]
-> 1862 module = _load_backend(backend_str)
1863 _backends[backend_str] = module
1864 return module
File ~/work/cvxjson/cvxjson/.venv/lib/python3.10/site-packages/pandas/plotting/_core.py:1831, in _load_backend(backend)
1826 if hasattr(module, "plot"):
1827 # Validate that the interface is implemented when the option is set,
1828 # rather than at plot time.
1829 return module
-> 1831 raise ValueError(
1832 f"Could not find plotting backend '{backend}'. Ensure that you've "
1833 f"installed the package providing the '{backend}' entrypoint, or that "
1834 "the package has a top-level `.plot` method."
1835 )
ValueError: Could not find plotting backend 'plotly'. Ensure that you've installed the package providing the 'plotly' entrypoint, or that the package has a top-level `.plot` method.
data = yf.download(tickers = "SPY AAPL GOOG MSFT", # list of tickers
period = "10y", # time period
interval = "1d", # trading interval
prepost = False, # download pre/post market hours data?
repair = True) # repair obvious price errors e.g. 100x?
[*********************100%***********************] 4 of 4 completed
prices = data["Adj Close"]
capital = 1e6
b = builder(prices=prices, initial_cash=capital)
for time, state in b:
# each day we invest a quarter of the capital in the assets
b[time[-1]] = 0.25 * state.nav / state.prices
portfolio = b.build()
portfolio.profit.cumsum().plot()
portfolio.nav.plot()
Rebalancing#
Usually we would not execute on a daily basis but rather rebalance every week, month or quarter. There are two approaches to deal with this problem in cvxsimulator.
Resample the existing daily portfolio (helpful to see effect of your hesitated trading)
Trade only on days that are within a predefined grid (most flexible if you have a rather irregular grid)
Resample an existing portfolio#
portfolio_resampled = portfolio.resample(rule="M")
frame = pd.DataFrame({"original": portfolio.nav, "monthly": portfolio_resampled.nav})
frame
| original | monthly | |
|---|---|---|
| Date | ||
| 2013-05-30 | 1.000000e+06 | 1.000000e+06 |
| 2013-05-31 | 9.945921e+05 | 9.945921e+05 |
| 2013-06-03 | 1.000400e+06 | 1.000391e+06 |
| 2013-06-04 | 9.917402e+05 | 9.917309e+05 |
| 2013-06-05 | 9.846364e+05 | 9.846127e+05 |
| ... | ... | ... |
| 2023-05-23 | 7.505451e+06 | 7.493060e+06 |
| 2023-05-24 | 7.461416e+06 | 7.447306e+06 |
| 2023-05-25 | 7.603349e+06 | 7.590982e+06 |
| 2023-05-26 | 7.711937e+06 | 7.698989e+06 |
| 2023-05-30 | 7.735463e+06 | 7.721169e+06 |
2518 rows × 2 columns
print(portfolio_resampled.stocks)
AAPL GOOG MSFT SPY
Date
2013-05-30 17854.390520 11527.266005 8564.268967 1814.033248
2013-05-31 17854.390520 11527.266005 8564.268967 1814.033248
2013-06-03 17895.614489 11573.477982 8432.882983 1831.100869
2013-06-04 17895.614489 11573.477982 8432.882983 1831.100869
2013-06-05 17895.614489 11573.477982 8432.882983 1831.100869
... ... ... ... ...
2023-05-23 10590.586692 16651.905094 5882.633509 4316.566726
2023-05-24 10590.586692 16651.905094 5882.633509 4316.566726
2023-05-25 10590.586692 16651.905094 5882.633509 4316.566726
2023-05-26 10590.586692 16651.905094 5882.633509 4316.566726
2023-05-30 10590.586692 16651.905094 5882.633509 4316.566726
[2518 rows x 4 columns]
# almost hard to see that difference between the original and resampled portfolio
frame.plot()
# number of shares traded
portfolio_resampled.trades_stocks.iloc[1:].plot()
Trade only days in predefined grid#
b = builder(prices=prices, initial_cash=capital)
# define a grid
grid = resample_index(prices.index, rule="M")
for time, state in b:
# each day we invest a quarter of the capital in the assets
if time[-1] in grid:
b[time[-1]] = 0.25 * state.nav / state.prices
else:
# forward fill an existing position
b[time[-1]] = b[time[-2]]
portfolio = b.build()
portfolio.nav.plot()
# Trading only once a month can lead to days where 150k had to be reallocated
portfolio.turnover.iloc[1:].plot()
Why not resampling the prices?#
I don’t believe in bringing the prices to a monthly grid. This would render it hard to construct signals given the sparse grid. We stay on a daily grid and trade once a month.